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438 results for “3D imaging”
NIS3D: A Completely Annotated Benchmark for Dense 3D Nuclei Image Segmentation
<p>NIS3D is an image segmentation benchmark containing over 22,000 manually annotated cells.</p> <p>Find more details here: https://github.com/yu-lab-vt/NIS3D</p>
Annotated and processed 3D confocal microscopy images of dorsal aorta in wild-type and Endoglin-deficient zebrafish embryos at 48 hpf and 72 hpf
<p>This repository contains the original 3D confocal microscopy images that were used for the analysis of vessel geometry and endothelial cell morphology in the dorsal aorta of wild-type and Endoglin-deficient zebrafish embryos at 48 hours post fertilization (hpf) and 72 hpf in the article <a href="https://www.biorxiv.org/content/10.1101/2024.02.19.580931">Novel mathematical approach to accurately quantify 3D endothelial cell morphology and vessel geometry based on fluorescently marked endothelial cell contours: Application to the dorsal aorta of wild-type and Endoglin-deficient zebrafish embryos</a>. In this article, we developed a novel mathematical approach that allows to consistently estimate 3D vessel geometry and endothelial cell surface morphology using only information from endothelial cell contours. For the article's analysis, endothelial cell contours were manually annotated on Pecam1-EGFP-labeled cell junctions. Furthermore, dorsal aorta cross-sections were outlined on Dextran Texas Red-perfused vessel lumens. Further details are provided in the article's Materials and methods section.</p> <p>This repository contains 14 images of 7 wild-type embryos, each imaged at 48hpf and 72hpf. Furthermore, 12 images of 6 Endoglin-deficient embryos, each imaged at 48hpf and 72hpf are included. These combined files (called "analysis data" in the article) are stored in "eng_wt_data.zip". Secondly, images of 2 wild-types at 72hpf with repeated cell contour annotation and outlined vessel lumens (called "validation data" in the article) are located in "wt_angiogram_data.zip". The provided files are stored in Imaris format and can be inspected using the free <a href="https://imaris.oxinst.com/imaris-viewer">Imaris Viewer software</a>.</p> <p>To allow inspection of the endothelial cell contours that we manually annotated for the article's analysis and compare them against the intermediate results of our novel mathematical approach, i.e., contour enrichments by neighboring cells, contour smoothing splines and their projections onto the estimated vessel surfaces, we imported these contours into the Imaris files. Note that the contours' coordinates in these files are slightly less precise than in our article's analysis and thus are intended for visual inspection. To exactly reproduce the results in our article, refer to the files in <a href="https://doi.org/10.5281/zenodo.10549101">our other Zenodo repository</a>.</p>
3D dynamic synchrotron images of sandstone rock and trained weights of the P3T-Net
<p>These are the dataset and trained models to demo the ability of P3T-Net, including:</p> <ol> <li>A trained model for the semantic indication module, which uses a U-ResNet architecture: Semantic_Indication_Modules.pt</li> <li>A trained model for the domain transfer module, which uses a CycleGAN-type architecture: Domain_transfer.pt</li> <li>A trained model for misalignment fixing, which uses a GAN-based architecture: Misalignment_Fixing_Module.pt</li> <li>A noisy synchrotron scan of a sandstone image under a core flooding condition.</li> </ol> <p>These are the pretrained weight that can be directly applied to transfer the dynamic synchrotron sandstone image to a clean version. The source code of P3T-Net can be found: https://github.com/KunningTang1/P3T-Net-for-3D-large-image-transfer.git</p>
Demo dataset for code to quantify the 3D biofilm biovolume in images
<p>This dataset contains the raw and analyzed images that can be used as a test/demo dataset for running the code to compute the 3D biofilm biovolume. The code is stored on this Gitlab repository: https://github.com/knutdrescher/biofilm-3D-biovolume </p>
Fluid Interfaces in Mixed-Wet Bead Packs: Insights from 3D X-Ray Imaging
<p>Data for beads curvature: The data on mean curvature on a 200×140×170 voxel dry image before and after the removal of any points within 5 voxels of bead contacts. </p> <p> </p> <p>Data for fluid interfacial curvature between oil and brine: The data on mean and Gaussian curvature distribution on a 930×930×860 voxel wet image. The curvature data is provided for both before and after the removal of points within one voxel of the three-phase contact line.</p>
Collected Colorimetric Microscopy (C-Microscopy) Images of Melanocytes and Melanoma 3D Spheroids Irradiated with Different Type of Proton Beam as Used in Proton Radiotherapy
<p>Collected Colorimetric Microscopy (C-Microscopy) images, color calibrated (D65 illuminant), of melanocytes and melanoma 3D spheroids, irradiated with different type of proton beam as used in proton radiotherapy.<br> <br>The data are supplement to:</p> <p>Martyna Durak-Kozica, Ewa Stępień, Jan Swakoń, Benedykt R. Jany, Kamil Kawoń, Damian Wróbel, Sebastian Kusyk, Małgorzata Grzesiak, Katarzyna Knapczyk-Stwora, Andrzej Wróbel, Joanna Chwiejand Paweł Moskal, Short-term response of melanoma spheroids and melanocytes to FLASH proton therapy - colorimetric and FTIR microscopy study, Pol J Med Phys Eng 2024;30(4):263-268 (2024) <a href="https://doi.org/10.2478/pjmpe-2024-0031">https://doi.org/10.2478/pjmpe-2024-0031</a></p> <p> </p> <p><br>HEMA-Spheroids-C-Microscopy.zip - melanocytes 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 435.87 microns</p> <p><br>WM-Spheroids-C-Microscopy.zip - melanoma 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 1089.68 microns</p> <p><br>WM-Spheroids-Texture-C-Microscopy.zip - surface texture of melanoma 3D spheroids, (C-Microscopy) images, color calibrated (D65 illuminant), image width 108.97 microns</p> <p> </p> <p>Proton Beam Radiotherapy Irradiation Conditions:</p> <p>C - Control</p> <p>CC - Control minus 7days</p> <p>LP - conventional proton radiotherapy (CONV) final dose 3Gy (dose rate about 0.140 Gy/s)</p> <p>F - FLASH proton radiotherapy final dose 3Gy (dose rate >60 Gy/s)</p> <p>F20 - FLASH proton radiotherapy final dose 20Gy (dose rate >60 Gy/s)</p> <p>F40 - FLASH proton radiotherapy final dose 40Gy (dose rate >60 Gy/s)</p> <p> </p> <p><br>The details about Colorimetric Microscopy (C-Microscopy) approach could be found in:</p> <p>Benedykt R. Jany, Quantifying Colors at Micrometer Scale by Colorimetric Microscopy (C-Microscopy) Approach, Micron 176, 103557 (2024) <a href="https://doi.org/10.1016/j.micron.2023.103557">https://doi.org/10.1016/j.micron.2023.103557</a></p>
Test Dataset for 3D semantic image segmentation of the Breast, Fibrograndular Tissue, and Breast Carcinoma
Open the record for dataset details and reuse information.
UTC STRIDE Project G2: Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation
<p>This repository contains the extracted traffic data used in the case study for UTC STRIDE project G2 "Quantitatively Evaluate Work Zone Driver Behavior Using 2D Imaging, 3D LiDAR, and Artificial Intelligence in Support of Congestion Mitigation Model Calibration and Validation". The traffic data was extracted using manual or AI-based methods (presented in the project final report) from two 30-minute videos with high and low traffic density. </p> <p>Below are the description of each data file:</p> <ul> <li><strong>high_density_both_lane_raw_AI_speed.csv</strong> <ul> <li><strong>Description: </strong>extracted individual vehicle speed data of the high traffic density video using the AI-based method.</li> <li><strong>Data Fields:</strong> <ul> <li>object_id: unique id for each detected and tracked vehicle</li> <li>frame_index: frame index of the video when other tracked vehicle exit the virtual speed loop.</li> <li>lane_id: lane id (1=outer lane, 2=inner lane)</li> <li>speed: average speed traveling through the virtual speed loop (kph)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_AI_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>AI extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>time (in min): i th minute in the 30-minute video.</li> <li>Count: number of vehicles counted in that minute of video.</li> <li>Speed (KPH): average vehicle speed in that minute of video.</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the high traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>high_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the high traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the inner lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_inner_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the inner lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count.csv</strong> <ul> <li><strong>Description: </strong>manually extracted individual vehicle speed, and headway data on the outer lane of the low traffic density data </li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video when the vehicle is recorded</li> <li>Vehicle Type: 1=passanger car, 2=pick/single unit truck, 3=semi-truck</li> <li>Enter Frame: frame index when the vehicle enters the virtual speed loop</li> <li>Exit Frame: frame index when the vehicle exits the virtual speed loop</li> <li>Speed (MPH): average speed over the virtual speed loop</li> <li>Time headway (s): time headway to the previous vehicle (if available)</li> </ul> </li> </ul> </li> <li><strong>low_density_outer_lane_traffic_manual_count_1min_bin.csv</strong> <ul> <li><strong>Description: </strong>manually extracted count, and speed data on the outer lane of the low traffic density data (aggregated into 1-minute bins)</li> <li><strong>Data Fields:</strong> <ul> <li>Minute #: i th minute in the 30-minute video.</li> <li>Count Total: number of vehicles counted in that minute of video.</li> <li>Speed Avg (MPH): average vehicle speed in that minute of video, in mph.</li> <li>Speed Avg (KPH): average vehicle speed in that minute of video, in kph</li> <li>Count Type 1 (Car): type 1 vehicle count</li> <li>Count Type 2 (Pickup): type 2 vehicle count</li> <li>Count Type 3 (Semi): type 3 vehicle count</li> </ul> </li> </ul> </li> </ul>
3D images of fossil planktonic foraminifera from the western Pacific Ocean: a database concerning two biostratigraphic events during the Early Pleistocene
<p>Here we present planktonic foraminifera X-ray images dataset during 1.72-2.15 million years ago using Microfocus X-ray CT (MXCT) and Projection X-ray Microscopy (PXM) technologies in a sedimentary core ODP Hole 1115B (9 11'S, 151 34E, water depth 1149 m) in the Solomon Sea. The species Globigeerinoideseela fistuolsa, Trilobatus sacculifer, and Pulleniatina spp. tests were hand-picked and gently cleaned for X-ray images. In total, there are 20 individuals with 20 images are presented in this dataset.-</p>
Parcel2D Real - A real-world image dataset of cuboid-shaped parcels with 2D and 3D annotations
<p>Real-world dataset of ~400 images of cuboid-shaped parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>.</p> <p>Relevant computer vision tasks:</p> <ul> <li>bounding box detection</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction (.binvox files)</li> <li>3D reconstruction (.obj files)</li> </ul> <p>For details, see our <a href="https://ieeexplore.ieee.org/abstract/document/10069342">paper</a> and <a href="https://a-nau.github.io/parcel2d/">project page</a>.</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@inproceedings{naumannScrapeCutPasteLearn2022, title = {Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics}, author = {Naumann, Alexander and Hertlein, Felix and Zhou, Benchun and Dörr, Laura and Furmans, Kai}, booktitle = {{{IEEE Conference}} on {{Machine Learning}} and Applications ({{ICMLA}})}, date = 2022 }</code></pre> <p> </p>
Parcel3D - A Synthetic Dataset of Damaged and Intact Parcel Images with 2D and 3D Annotations
<p>Synthetic dataset of over 13,000 images of damaged and intact parcels with full 2D and 3D annotations in the <a href="https://cocodataset.org/#format-data">COCO format</a>. For details see our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and for visual samples our <a href="https://a-nau.github.io/parcel3d/">project page</a>.</p> <p><br> Relevant computer vision tasks:</p> <ul> <li>bounding box detection</li> <li>classification</li> <li>instance segmentation</li> <li>keypoint estimation</li> <li>3D bounding box estimation</li> <li>3D voxel reconstruction</li> <li>3D reconstruction</li> </ul> <p> </p> <p>The dataset is for <strong>academic research use only</strong>, since it uses resources with restrictive licenses.<br> For a detailed description of how the resources are used, we refer to our <a href="https://openaccess.thecvf.com/content/CVPR2023W/VISION/html/Naumann_Parcel3D_Shape_Reconstruction_From_Single_RGB_Images_for_Applications_in_CVPRW_2023_paper.html">paper</a> and <a href="https://a-nau.github.io/parcel3d/">project page</a>.</p> <p>Licenses of the resources in detail:</p> <ul> <li><a href="https://research.google/resources/datasets/scanned-objects-google-research/">Google Scanned Objects</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (for details on which files are used, see the respective <em>meta </em>folder)</li> <li><a href="https://zenodo.org/record/8041823">Cardboard Dataset</a>: <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></li> <li><a href="https://ieeexplore.ieee.org/abstract/document/8999123">Shipping Label Dataset</a>: <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC 4.0</a></li> <li>Other Labels: See file <em>misc/source_urls.json</em></li> <li><a href="https://github.com/weberhen/learning_indoor_lighting">LDR Dataset</a>: License for Non-Commercial Use</li> <li><a href="https://data.vision.ee.ethz.ch/sagea/lld/">Large Logo Dataset (LLD)</a>: Please notice that this dataset is made available for academic research purposes only. All the images are collected from the Internet, and the copyright belongs to the original owners. If any of the images belongs to you and you would like it removed, please kindly inform us, we will remove it from our dataset immediately.</li> </ul> <p>You can use our textureless models (i.e. the <em>obj</em> files) of damaged parcels under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a> (note that this does not apply to the textures).</p> <p> </p> <p>If you use this resource for scientific research, please consider citing</p> <pre><code>@inproceedings{naumannParcel3DShapeReconstruction2023, author = {Naumann, Alexander and Hertlein, Felix and D\"orr, Laura and Furmans, Kai}, title = {Parcel3D: Shape Reconstruction From Single RGB Images for Applications in Transportation Logistics}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2023}, pages = {4402-4412} }</code></pre>
Imaging 3D Chemistry at 1 nm Resolution with Fused Multi-Modal Electron Tomography
<p>Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment completes. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one nanometer resolution in a Au-Fe<sub>3</sub>O<sub>4</sub> metamaterial, Co<sub>3</sub>O<sub>4</sub> - Mn<sub>3</sub>O<sub>4</sub> core-shell nanocrystals, and ZnS-Cu<sub>0.64</sub>S<sub>0.36</sub> nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX / EELS) signals. Now sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.</p>
L2LFlows: Generating High-Fidelity 3D Calorimeter Images
<p>This upload contains the datasets used in <a href="https://arxiv.org/pdf/2302.11594.pdf">arXiv:2302.11594</a>. The file <em>g4-showers_950k_10x10_train_val_test.pt</em> contains the <strong>760k training</strong>, <strong>95k validation</strong> and <strong>95k test</strong> showers as well as their incident energies. It should be loaded as follows: </p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p><em>import torch </em></p> <p><em>list_tensors = torch.load(args.file_path)</em></p> <p><em>for (idx, tensor) in enumerate(list_tensors):</em></p> <p><em> [showers_train, showers_val, showers_test, inc_energies_train, inc_energies_val, inc_energies_test] = list_tensors</em></p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p>The file <em>g4-showers_665k_10x10_test.pt</em> contains 665k additional showers that were used for the classifier scaling studies, in addition to the 95k test showers from the file <em>g4-showers_950k_10x10_train_val_test.pt</em>. It should be loaded as follows: </p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p><em>import torch</em></p> <p><em>list_tensors = torch.load("g4-showers_950k_10x10_train_val_test.pt") </em></p> <p><em>[showers_geant, inc_energies_geant] = list_tensors</em></p> <p>- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - </p> <p>A detailed description of how the datasets were simulated can be found in the paper. </p>
Images and 3D digitisations of Branding Heritage #3
<p>These files are 3D digitisations and images of Branding Heritage</p>
Images and 3D digitisations of Branding Heritage #4
<p>These files are 3D digitisations and images of Branding Heritage</p>
3D-Imaging and Quantitative Subsurface Dielectric Constant Measurement Using Peak Force Kelvin Probe Force Microscopy
<p>We demonstrate a new approach to simultaneously measure the dielectric constants of buried structures and interfaces by combining peak force tapping quantitative Nano-mechanical mapping (PF QNM) and frequency-modulated Kelvin probe force microscopy (FM-KPFM). The developed method paves the way for 3D-imaging of dielectric constants of composite materials and heterostructures.</p>
Images and 3D digitisations of Branding Heritage #5
<p>These files are 3D digitisations and images of Branding Heritage</p>
Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment
<p>These are 3D live-cell imaging datasets of gastric tumor organoids in co-culture with primary human Natural Killer (NK) cells. The datasets were analyzed by a new, deep learning-based 3D image analysis software tool, SiQ-3D, which we developed and presented in the paper titled "Deep Learning-based 3D single-cell imaging analysis pipeline for quantifying cell-cell interaction dynamics in the tumor microenvironment". Interested users can download the SiQ-3D software code from GitHub (https://github.com/simonlbd1/SiQ-3D) or Code Ocean (https://codeocean.com/capsule/6676007/tree/v2), analyze the 3D image datasets locally, and cross-check the results with the SiQ-3D quantified results that we provided here.</p>
3D Breast Imaging for Cosmetic and Reconstructive Breast Surgery
ClinicalTrials.gov study NCT01964105. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Real Time 3D Imaging and Surrogate Bone Model
ClinicalTrials.gov study NCT02204007. IPD Sharing: NO. Countries: 1. Publications: 15.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.